Oil is marching toward $100 a barrel, largely due to China’s boom. Demand for ethanol is pushing corn prices up. Soybean prices are rising because so many bean fields have been switched to corn. In short, prices of commodities worldwide are soaring, making for frenzied action in commodities futures markets, where traders buy and sell contracts setting prices to be paid months down the road.

Everyone knows that supply and demand govern prices. But much remains unclear about the subtleties of that interaction in the commodities markets — and about the best ways to wring profits from it. “Commodities futures are an asset class that has been under-researched compared to equities in particular,” says Wharton finance professor Gary B. Gorton.

New work by Gorton, Fumio Hayashi of the University of Tokyo and K. Geert Rouwenhorst of Yale, shows how investors can win bigger profits with futures-trading strategies based on the amount of a given commodity that is held in storage. Returns — or “risk premiums” — are bigger when low inventories make prices more volatile, Gorton and his colleagues conclude.

Over the period studied, 1990 through 2006, a trading strategy focusing on low-inventory commodities would have produced average annual returns of 13.34%, compared to 4.62% for a high-inventory strategy, and about 9% for an approach that did not take inventory levels into account. “What we show is that you get paid more if the risk is higher,” Gorton says.

Most importantly, the research shows how to gauge hard-to-measure inventory levels by looking at the relationship between prices on the futures market and the spot, or cash, market where commodities are bought for immediate delivery. When the spot price rises more than the futures price, traders can infer that the inventory level has fallen — and that the opportunities to earn outsized profits are especially good. The findings, which could be especially useful to institutional investors and commodities trading advisors (CTAs), are described in a paper titled, “The Fundamentals of Commodity Futures Returns.”

The new paper follows on Gorton and Rouwenhorst’s earlier work, which found that, over long periods, commodity returns are as high as those of stocks, while experts had long thought them to be lower. That work, described in a paper titled “Facts and Fantasies about Commodities Futures,” also concluded, surprisingly, that commodities are not as risky as stocks. “Part of what we’re doing is trying to understand the determinant of those returns,” Gorton says about the follow-up study. “We get much more specific… [to] see if we can explain what moves that risk premium around through time and on individual commodities.”

Insurance against Price Declines

The work comes as commodities markets are booming. Rapid economic growth in China, India and other countries has spurred demand for oil, metals and many other commodities, as has use of grain for ethanol, a gasoline alternative. The action has attracted more investors. New commodities exchanges are being established and older ones are expanding and converting to for-profit models searching for new products to trade, such as futures contracts.

Heavy demand means that many commodity inventories are especially low. That makes prices more volatile because commodities buyers are less certain of future supplies.

Commodities producers are thus drawn to the futures markets, which provide insurance against price declines by locking in tomorrow’s prices today. Investors are eager to earn the premiums that come from providing that insurance through futures contracts. As a result of all this, futures contracts have become more liquid, or easy to trade, Gorton says. “The whole futures industry is expanding by an amazing amount.”

A futures contract is an agreement for the seller to deliver a specified quantity of a commodity on a certain date for a price agreed upon in the present. Once created, a standardized contract can be traded in the secondary market. In practice, only about 2% of contracts are used to effect an actual delivery of the commodity. In most cases, the parties simply buy or sell other contracts to offset the obligations incurred earlier. Their profits or losses are determined by the differences in prices between the two contracts.

A corn futures contract traded at the Chicago Board of Trade, for example, provides for delivery of 5,000 bushels of corn. Recently, contracts for delivery in March 2008 traded at a price of about $3.90 per bushel. The holder of the contract, therefore, is obligated to pay $19,500 to buy 5,000 bushels next March.

A producer, such as a corn farmer, might write, or sell, such a contract to lock in a price for a sale at a later date. That way, the farmer is insured against the risk that the market, or spot, price falls. On the other side of the deal, the investor or speculator is providing the insurance and hopes the price will rise.

If the price next March is $4 a bushel, the investor will make 10 cents a bushel, or $500 per contract, using the contract to buy — for $3.90 — corn that can be sold for $4. The farmer would, in effect, have paid, or given up, 10 cents a bushel to avoid the risk of getting less than $3.90. If the price falls to $3.80, the contract holder would lose 10 cents a bushel, or $500 per contract, and the farmer would avoid a $500 loss.

In their earlier work, Gorton and Rouwenhorst looked at 36 commodity futures markets over 45 years. They found that, on an annualized basis, futures contracts underestimated future spot prices by 5%, offering a healthy return for futures investors.

The new work looks at the key role played by inventories — commodities stored for future sale. Inventories serve as buffers against fluctuations in supply, and the Theory of Storage says that when inventories are low, spot prices are more volatile, since commodity buyers cannot be certain of sufficient supplies.

Futures markets provide insurance against future price volatility. So, when low inventories heighten the risk of price volatility, the cost of this insurance can be expected to rise. That translates into bigger returns for the contract holders who take on these bigger risks.

Predicting Inventory Levels

Gorton and his colleagues used inventory and futures pricing data on 31 commodities to see how two portfolios of futures contracts would have performed from 1969 through 2006. One portfolio was adjusted each month so that it always contained futures contracts for commodities with higher-than-normal inventories. It returned an average of 4.62% a year. The second portfolio, tracking low-inventory commodities, returned 13.34% a year. A third portfolio, a simple index composed of equal amounts of each commodity, returned 8.98% a year.

The low-inventory portfolio was slightly more risky than the simple, equally weighted index, but the extra risk was more than offset by the extra return, Gorton says.

By itself, this finding does not offer a useful trading strategy because inventory data is generally not available quickly enough. So the authors looked at ways to infer inventory levels from the futures- and spot-pricing data. The first approach looks at the “futures basis” — the difference between the spot price one would pay today and futures price one would agree upon today but not pay until later. When inventories fall, the spot price rises because supply is low relative to demand. The futures price rises as well but not so much, because traders believe inventories will gradually be replenished before the contracted delivery dates arrive. As a result, the gap between spot and futures prices widens.

Looking at data for the 31 commodities, the authors found that an above-average basis did indeed coincide with low inventories, and vice versa. “The empirical evidence is consistent with the prediction of the Theory of Storage, and suggests that the basis is a useful indicator of the level of a commodity’s inventory,” the authors conclude.

Similarly, they found that past returns on futures contracts are a good indicator of present inventory levels. High returns, for example, coincide with reduction in supply, which equates to reduced inventories. Traders, therefore, can use prices to determine when inventories have fallen to levels likely to make futures contracts especially profitable, Gorton and his colleagues note.

The authors created two portfolios to test this. For the first, they ranked commodities according to their futures basis. In this case, that was defined as the difference between the price in the contracts maturing the soonest minus the price in the contracts maturing after that — three months later, for example. The High Basis portfolio contained equal amounts of contracts for commodities in the top half of the rankings, and it was adjusted every month as the rankings changed. The authors found that from 1990 through 2006, this portfolio produced average annual returns of 14.67%, compared to about 9% for an index of futures contracts that did not take bases into account.

For the second portfolio, they ranked commodities according to their futures contract returns for the prior 12 months. The 50% with the highest returns were put into the portfolio and adjusted every month as the rankings changed. For the 1990-2006 period, this Relative Strength portfolio would have returned 16.67% a year. While both portfolios were slightly more risky than the index, this was more than compensated for by the outsized returns.

Finally, the authors looked at how their trading strategies would stack up against competition in the real world — the CTAs, which operate investment pools similar to hedge funds. For this they used the High Basis and Relative Strength portfolios, assuming an investor would buy these contracts, or go long. In addition, they assumed the investor would short, or sell, contracts that were in the bottom half of the two rankings. That strategy would profit from the poor performance of the commodities with low basis and poor relative strength. The study also assumed the investor would pay annual fees of 2% of assets, similar to those paid by CTA investors.

From 1990 through 2006, this approach would have returned an average of 10.88% a year, compared to 5.76% for a standard CTA performance index published by Barclay Trading Group. “If you constructed trading strategies based on these signals, you would do rather well,” Gorton said. “You would do much better than CTAs.”